Large language models have demonstrated varying levels of competence across a range of reasoning tasks, but coarse-grained evaluations often do not reflect their specific strengths and weaknesses, particularly in complex tasks such as Narrative Question Answering. In this paper, we advocate for a multi-dimensional skill-based evaluation that assesses models across distinct core skill dimensions. Our proposed skill-focused evaluation framework offers a granular and more realistic measure of model performance, revealing targeted areas for improvement and guiding future development. Experiments on Narrative Question Answering demonstrate that dimension-level analysis captures the multifaceted nature of the task and informs more effective model evaluation.